Matching process controllers for improved matching of process

ABSTRACT

Described herein are methods and systems for chamber matching in a manufacturing facility. A method may include receiving a first chamber recipe advice for a first chamber and a second chamber recipe advice for a second chamber. The chamber recipe advices describe a set of tunable inputs and a set of outputs for a process. The method may further include adjusting at least one of the set of first chamber input parameters or the set of second chamber input parameters and at least one of the set of first chamber output parameters or the set of second chamber output parameters to substantially match the first and second chamber recipe advices.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/890,802, filed Oct. 14, 2013, the entire contents of which are herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present application is related to the field of process control and,in particular, to chamber matching.

BACKGROUND

The need to achieve high-yield and throughput levels in the face ofever-decreasing feature size in nano-manufacturing requires improvedmatching of process chambers. Sustaining a fleet of tools to a matchedstate (i.e., chamber matching) can reduce yield losses and yieldvariability, allow for greater routing flexibility in the fab, identifyand control process inefficiencies, and reduce time for root causeanalysis of issues. Chamber matching is often achieved by using processcontrol (e.g., run-to-run control (R2R control)) to tune each chamberindependently from one another to provide the same process outputs, suchas target film thickness and uniformity. Unfortunately, only matchingthe process outputs of different chambers does not necessarily implythat the states of operation of the chambers are matched. To match thestates of operation requires that in addition to matching processoutputs, process conditions, such as process inputs and processvariables should be matched. An example of only matching the processoutputs is as follows: furnace chamber 1 could produce a film thicknessof 1000 Angstroms with a temperature setting of 100 degrees and a timesetting of 1 minute, while chamber 2 produces the same thickness of 1000Angstroms, with a higher temperature of 110 degrees and a shorter timeof 50 seconds. The result is that the chambers are not truly matchedfrom a processing standpoint, and ultimately such non-matched processessignificantly decrease yield and throughput.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be more readily understood from the detaileddescription of exemplary embodiments presented below considered inconjunction with the attached drawings, of which:

FIG. 1A illustrates an exemplary architecture of a manufacturingenvironment, in which embodiments of the present invention may operate;

FIG. 1B illustrates a multi-dimensional solution to chamber matching,according to some aspects of the present disclosure;

FIG. 2 provides a graphical illustration of chamber matching performedaccording to some aspects of the present disclosure;

FIG. 3 illustrates a graphical representation of state space as appliedto chamber matching, in accordance with one embodiment of the presentinvention;

FIG. 4 is a flow diagram of one embodiment of a method for chambermatching; and

FIG. 5 illustrates a block diagram of an exemplary computer system, inaccordance with one embodiment of the present invention.

DETAILED DESCRIPTION

Described herein is a mechanism for implementing an improved definitionof chamber matching that matches both input recipes and output valuesfor a process across chambers in a manufacturing facility in a chambermatching effort.

The term “chamber matching” has many definitions. One definition is thattwo chambers that produce the same output as measured by metrology arematched. That is, the outputs of the chambers are matched without regardto matching the inputs of the chambers. The chambers may be producingthe same metrology values in very different ways. For example, furnace 1uses high-temp and low-time while furnace 2 uses low-temp and high-timeto produce the same output (e.g., both produce the same film thicknessas measured by metrology), but with very different inputs.

Aspects of the present disclosure provide an improved definition ofchamber matching. In the improved definition of chamber matching,chambers are matched if the states of operation of the chambers arematched. Matching the states of operation of the chambers requires than,in addition to matching process outputs, process conditions such asprocess inputs and process variables are also matched. Processconditions, such as outputs and inputs cannot be exactly matched becausethere are subtle differences in chambers that generally cannot beavoided, such as time since last maintenance and age of variouscomponents. Such an improved definition of chamber matching betteraligns processing environments and in application, leads to moreconsistent quality in product across chambers. Moreover, the improveddefinition of chamber matching can improve the ability to control inputand output parameters across equipment for a process. Additionally, bymatching inputs across a process, both measured and unmeasured outputscan increase in consistency and similarity, ultimately helping toincrease yield. Hereafter, the term chamber matching refers to theimproved definition of chamber matching.

A typical run-to-run (R2R) controller is a model based controller. Forexample, the model can take the following form:Y=Ax+c,where:

Y=Vector of system outputs, usually measured with metrology, which maybe process targets such as thickness and uniformity,

x=Vector of system tunable inputs (e.g., portion of recipe),

A=Matrix of slope parameters for equation,

c=Vector of constant terms for linear model.

The model can be written as a set of equations:

y₁ = a₁₁x₁ + a₁₂x₂ + …  a_(1m)x_(m) + c₁ …y_(n) = a_(n 1)x 1 + a_( n 2)x₂ + …  a_(nm)x_(m) + c_(n)

Traditionally, R2R controllers often update model terms on an R2R basis(e.g., after each run), based on the difference between the predicted“y” (i.e., output) and the actual “y”. Methods such as ExponentialWeighted Moving Average (EWMA) are leveraged for smoothing andincorporating historical information into the update process. Forexample, each “c” can be updated at run “t” by:c_(t)=α(y_(t)−A_(xt))+(1−α)c_(t-1), where α is a forgetting factor(0≦α≦1).

Depending on the number of outputs being controlled and number oftunable inputs, the process matching problem can be underdetermined,exactly determined, or overdetermined. In practice, process matchingproblems are almost always underdetermined. If the solution isunderdetermined, an infinite number of solutions (i.e., sets of inputsthat produce the desired outputs, also called “advices”) exist. Theaforementioned is generally true only if a sufficient number of inputsare not “railed” (i.e., forced to stay at a limit value and therefore,not adjustable R2R). Technically, if the solution is overdetermined, anumber of solutions that generate the same outputs that are “close” tothe targets can be generated.

Traditionally, R2R controllers, when faced with an infinite solutionspace, choose the “advice” that is “closest” to the previous advice.Said differently, the next solution is chosen by its closeness to theprevious solution. Choosing the advice that is closest to the previousadvice can be determined with techniques such as constrained LeastSquares. Variables can be weighted relative to each other to adjust thedefinition of “closeness” to the particular application.

Aspects of the present disclosure provide a solution that not onlysatisfies the minimal definition of chamber matching but also theimproved definition of chamber matching, hereinafter called a chambermatching effort. That is, aspects of the present disclosure can matchprocess conditions, such as input recipes as well as output (e.g.,metrology) values in a chamber matching effort. For example, an exampleprocess includes tunable inputs (e.g., pressure and temperature) andoutputs (e.g., thickness). The tunable input types and output types maybe the same across chambers. That is, pressure and temperature may becontrolled at each chamber. However, the values (e.g., parameters) ofthe inputs may be tuned in order to obtain output targets. It should benoted, that output parameters may also be called output targets. In achamber matching effort, both the input values (e.g., input parameters)and output values (e.g., output parameters or targets) may be adjustedso that they approximate one another. For example, to maintain aconstant output value (e.g., thickness of 10 micrometers), pressure andtemperature values may be adjusted to approximate 10 Bar and 250° C.,respectively, across all the chambers in a chamber matching effort.Aspects of the present disclosure adjust the concept of closeness inchamber matching to incorporate recipe recommendations across chambers,so that outputs as well as inputs are matched as much as possible acrossa set of chambers. Aspects of the present disclosure leverage the factthat an R2R controller has a choice of recipe advices (i.e., a set ofinputs that produce the desired outputs), usually infinite, that canprovide an R2R control action.

Aspects of the present disclosure can determine a multi-chambercomposite average recipe advice (e.g., a target recipe, compositechamber recipe advice). A composite chamber recipe advice may be theaverage of all chamber recipes for a particular process. The compositerecipe advice may include tunable inputs and outputs, and a set ofcomposite input parameters and a set of output parameters to tune thetunable inputs and outputs, respectively. For example, tunable inputsmay be temperature and pressure and may have parameters of 100° C. and20 Bar, respectively. An output may be thickness and have a parameter ortarget of 10 Micrometers. A composite chamber recipe advice may be usedin a chamber matching effort where the recipe advice of each chamber isadjusted to approximate the composite chamber recipe advice. Forexample, the input parameters and the output parameters of a particularchamber recipe advice may be tuned to more closely approximate the inputparameters and output parameters of the composite chamber recipe advice.In another example, prior to each of a chamber's process run, anassociated R2R controller may send a R2R advice request to ensure thatthe chamber recipe advice is the closest to the composite recipe advice.All the R2R controllers may track (i.e., stay very close to) each otherwith respect to composite recipe advice while still providing R2Rcontrol.

Aspects of the present disclosure describe a continuous update of thecomposite recipe advice. After a chamber's associated R2R controllersends out an R2R advice request and in response, the chamber recipeadvice has been updated to approximate the composite recipe advice, thecomposite recipe advice may be further updated by incorporating thenewly updated chamber recipe advice into an updated composite recipeadvice.

Some aspects of the present disclosure describe weighting methods usedto favor or prioritize particular parameters (e.g., input and outputparameters) for matching. Aspects of the current disclosure alsodescribe how weighting methods work with enhanced R2R controlcapabilities such as, virtual metrology enhanced R2R control. Aspects ofthe current disclosure are not limited to fabrication, but may beapplied in any process, such as industrial controls and manufacturing,generally.

According to some aspects of the present disclosure, chamber matchingmay be described mathematically. A set of ‘k’ chambers to be matched isdefined. An R2R controller for each chamber may use the followingequations to describe outputs and inputs:

y₁ = a₁₁x₁ + a₁₂x₂ + …  a_(1m)x_(m) + c₁ …y_(n) = a_(n 1)x 1 + a_( n 2)x₂ + …  a_(nm)x_(m) + c_(n)The physical parameter related to each y_(i) (1≦i≦n) and x_(j) (1≦j≦m)is the same for all chambers (1 through k). Note the values for “a” and“c” are not required to be the same for each chamber.

According to some aspects of the present disclosure, determining thecomposite chamber recipe advice across chambers is performed byaveraging each input parameter across the chambers. In some embodiments,determining the composite chamber recipe advice across chambers includesaveraging the models across each chamber of a group of chambers andinverting the composite model to determine the average recipe. Theaverage recipe advice is one type of composite recipe advice, X_(c).

According to some aspects of the present disclosure, every time there isan R2R control model adjustment at any chamber, the chamber matchingmethod can determine the recipe advice that meets the control criteriathat is the closest to the composite advice, X_(c). For example,minimize ∥X−X_(c)∥² and update X_(c), given the new recipe advice X. Inanother embodiment, the recipe advices of each chamber in the matchinggroup are tied to the other chambers.

According to some aspects of the present disclosure, parameters (e.g.,tunable inputs and outputs) are weighted to contribute more or less tothe averaging and movement towards average. For example, in an exemplaryrecipe advice the power settings may be weighted more than the timesettings. The weighting of input parameters (e.g., tunable inputs) oroutputs (e.g., outputs) can allow a chamber's inputs to be matched toparameters that are most important to the customer. For example,pressure can be weighted more heavily than power so that the chambers,while producing the same output, also use the same or similar pressureinput. Alternatively, the weighting of input to output parameters canallow matching of input parameters at the expense of output parametermatching. Said differently, in some instances controlling inputs may bemore important than controlling the output. The weighting of individualchambers can dictate the level at which each chamber contributes to thematching or derives advice from the matching. For example, a particularprocess chamber may be near ideal (e.g., a “golden” chamber), and may beweighted more heavily than other less ideal chambers when calculatingthe composite recipe advice.

According to some aspects of the present disclosure, chamber matchingcan be used with enhanced R2R control techniques, such as virtualmetrology aided R2R control.

In the following description, numerous details are set forth. It will beapparent, however, to one skilled in the art, that the present inventionmay be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form,rather than in detail, in order to avoid obscuring the presentinvention.

FIG. 1A illustrates an exemplary architecture 100 of a manufacturingenvironment, in which embodiments of the present invention may operate.The manufacturing environment may be a semiconductor manufacturingenvironment, an automotive manufacturing environment, etc. In oneembodiment, the architecture 100 includes one or more supply chaindatabases 120, one or more customer databases 115, a manufacturingexecution system (MES) 110 and a manufacturing information and controlsystem (MICS) 105, connected via a network 125.

The network 125 may be a public network (e.g., Internet), a privatenetwork (e.g., Ethernet or a local area Network (LAN)), or a combinationthereof. The network 125 may include multiple private networks, whichmay be directly connected or connected via a public network. Forexample, the supply chain database 120 may be connected to a firstprivate network controlled by suppliers of the manufacturingenvironment, the customer database 115 may be connected to a secondprivate network controlled by a customer of the manufacturingenvironment, and the MICS 105 and MES 110 may be connected to a thirdprivate network. Each of these private networks may be connected via apublic network.

A supply chain database 120 includes information available to and/orprovided by a supplier or distributor. Such information may include, forexample, a supplier's orders (e.g., of parts and goods), supplierinventory (e.g., current inventory, projected inventory, etc.),projected delivery dates, etc. Where materials are received frommultiple distributors or suppliers, architecture 100 may includemultiple supply chain databases 120. For example, a first supply-chaindatabase may include information on raw goods, and a second supply-chaindatabase may include information on manufacturing equipment.

A customer database 115 includes information available to and/orprovided by a customer. Such information may include, for example,customer demand for specified articles of manufacture, customerinventory, etc. Architecture 100 may include a single customer database115 for multiple customers, or multiple customer databases 115, each ofwhich provides information about a distinct customer.

The manufacturing execution system (MES) 110 is a system that can beused to measure and control production activities in a manufacturingenvironment. The MES 110 may control some production activities (e.g.,critical production activities) or all production activities of a set ofmanufacturing equipment (e.g., all photolithography equipment in asemiconductor fabrication facility), of a manufacturing facility (e.g.,an automobile production plant), of an entire company, etc. The MES 110may include manual and computerized off-line and/or on-line transactionprocessing systems. Such systems may include manufacturing machines,metrology devices, client computing devices, server computing devices,databases, etc. that may perform functions such as processing, equipmenttracking, dispatching (e.g., determining what material goes to whatprocesses), product genealogy, labor tracking (e.g., personnelscheduling), inventory management, costing, electronic signaturecapture, defect and resolution monitoring, key performance indicatormonitoring and alarming, maintenance scheduling and so on. In oneembodiment, the MES 110 is connected with one or more MES data stores130. The MES data stores 130 may be databases, file systems, or otherarrangements of data on nonvolatile memory (e.g., hard disk drives, tapedrives, optical drives, etc.), volatile memory (e.g., random accessmemory (RAM)), or combination thereof. Each MES data store 130 maystore, for example, historical process information of manufacturingrecipe advices (e.g., temperatures, pressures, chemicals used, processtimes, etc.), equipment maintenance histories, inventories, etc.

The manufacturing information and control system (MICS) 105 combinesdisparate information from multiple different sources (e.g., datastores), and presents this information in a single interface. The MICS105 can be used to gain an understanding of the manufacturingenvironment, and can enable a user to determine an efficiency of themanufacturing environment and/or how to improve all or components of themanufacturing environment. The MICS 105 may be used to control chambermatching efforts in a manufacturing environment. The MICS 105 may alsodraw inferences from, report out, and/or act upon the combinedinformation. For example, MICS 105 can act to determine differentchamber recipe advices, adjust different chamber recipe advices,generate composite chamber recipe advices, provide bottleneck analysis,provide asset management (e.g., reduce unscheduled equipment downtime),improve lean practices, etc. In one embodiment, MICS 105 includes a dataconsolidator 140, a decision support logic component 155, an executionlogic component 160, a predictor 150, a real-time monitor 165, and achamber matching subsystem 145.

The data consolidator 140 consolidates received data of manufacturingequipment (e.g., chambers) from multiple different sources (e.g., datastores). The manner in which the received data from the data sources isconsolidated may be dependent upon relationships between the receiveddata. Such relationships may be user defined. Moreover, sources fromwhich data is consolidated, and the manner in which data isconsolidated, can be user configurable. Therefore, as new data storesare added and/or old data stores are removed, data consolidator 140 maybe adapted to accommodate the change.

In one embodiment, data consolidator 140 consolidates data from multipleMES data stores 130 (e.g., an inventory data store, a maintenance datastore, a metrology data store, process data stores, etc.). In a furtherembodiment, the data consolidator 140 consolidates data from the supplychain databases 120 and/or customer databases 115. In yet a furtherembodiment, data consolidator 140 consolidates real-time data as thedata is collected by real-time monitor 165 (e.g., from manufacturingmachines (chambers) and metrology machines). In still a furtherembodiment with a predictive model, data consolidator 140 consolidatesvirtual data that has been generated by predictor 150. Data consolidator140 can also consolidate manually entered data (e.g., data entered by adevice operator, maintenance personnel, etc.). In another embodiment,data consolidator 140 consolidates past and present recipe advices fork-number of chambers or manufacturing equipment.

In one embodiment, data consolidator 140 stores consolidated received(actual) data in MICS data store 135. Alternatively, data consolidator140 may store a subset of all consolidated received data in MICS datastore 135. For example, data consolidator 140 may store consolidateddata necessary to generate a composite chamber recipe in MICS data store135.

Real-time monitor 165 collects real-time data that includes presentvalues of one or more equipment parameters (e.g., input and outputparameters). Such real-time data may be collected from sensors andsystems to which MICS 105 is connected via network 125. Real-timemonitor 165 may, for example, collect data from manufacturing equipment(e.g., chambers) and metrology equipment (e.g., chambers) as the data isgenerated. In one embodiment, real-time monitor 165 provides thereal-time data to data consolidator 140.

Decision support logic component 155 can provide recommendations anddecisions on chamber matching based on the historical and currentoperational status (e.g., received data of past and present chamberrecipe advices). Decision support logic component 155 may also providerecommendations and decisions on chamber matching based on futureoperational status (e.g., desired output values and/or virtual data).The decision support logic component 155 may provide suchrecommendations and decisions based on business logic that matches a setof values with an outcome. The outcome may, for example, causemaintenance personnel to be notified of a pending machine failure, causea process engineer to be notified of abnormal measurement results, etc.The outcome may also recommend actions to be taken. For example, theoutcome may recommend that particular maintenance be performed on amachine.

Execution logic component 160 can be responsible for taking action onthe business systems based on the output of the decision support logiccomponent 155. These actions are in the form of intelligent businessrules that can be launched either through real-time system events,predicted events, or scheduled activities. For example, execution logiccomponent 160 may automatically schedule maintenance for a machine whencertain values are detected, automatically shut down the machine, etc.

MICS 105 may also include a chamber matching subsystem (CMS) 145. TheCMS 145 uses the data in the MICS data store 135 to perform chambermatching efforts for various tools and chambers in the manufacturingenvironment. Chamber matching can be performed by components of the MES110 which may forward the associated process data to the MICS 105.Alternatively or in addition, chamber matching can be performed by theMICS 105 based on equipment parameters (e.g., tunable inputs, outputs,input parameters, output parameters) received from the MES 110 and/orfrom other sources in the manufacturing environment. Chamber matchingcan be performed using specific chamber methods (e.g., state spacecontrol, EWMA, etc.), which can be static or change over time. The CMS145 may also perform analysis of equipment data stored in the MICS datastore 135 to define a composite recipe advice.

In one embodiment, the CMS 145 provides a GUI that allows a user toselect a specific tool, presents outputs and tunable inputs availablefor the selected tool, and allows the user to select and tune one ormore of the presented outputs and tunable inputs. The CMS 145 may allowthe user to tune the set of tunable inputs and the set of outputs byadjusting input and output parameters.

In some embodiments, the CMS 145 controls a set of l′ chambers to bematched. The number of chambers, ‘k,’ may be defined by a user or aprocess administrator of MICS 105. The CMS 145 may define an R2Rcontroller for each chamber in the form of an equation, for example anumber of outputs defined by a number of tunable inputs and a chamberconstant. The CMS 145 may tune the outputs by using output parametersand may tune the inputs by using input parameters. For example, in asemiconductor process, wafer thickness may be the output while time andtemperature may be the controllable inputs. Continuing the example, theCMS 145 may perform chamber matching by tuning the time and temperatureparameters so that both the output (e.g., the defined thickness) and theinputs (e.g., the values of time and temperature) converge across the‘k’ chambers.

In some embodiments, the CMS 145 determines a composite recipe adviceacross ‘k’ chambers. For example, the CMS 145 may simply average eachinput coefficient across the ‘k’ chambers to determine a compositerecipe advice. In another example, the CMS 145 may average the recipeadvices across ‘k’ chambers to determine a composite recipe advice. Inyet another example, a user may define an alternative method ofcomputing a composite recipe advice for CMS 145 to perform. The CMS maycommunicate with MES 110, to control the manufacturing equipment so thatthe inputs and outputs of the ‘k’ chambers converge as a result of thecomposite recipe advice.

In one embodiment, the CMS 145 may weight certain tunable inputs and/oroutputs more or less across the ‘k’ chambers when determining thecomposite recipe advice. Additionally, the CMS 145 may weight aparticular chamber more or less when determining the composite recipeadvice. In one embodiment, the CMS 145 may determine the differentweights automatically. For example, if the input, ‘time’, has beenweighted by a user, the CMS may automatically adjust the weights ofinputs and outputs to ensure that the additional weight for ‘time’ hasbeen appropriately accounted for. In an alternate embodiment, a user maydefine different weights and input those weights into MICS 105 and/orCMS 145.

In some embodiments, any time an R2R control model adjustment at anychamber is made, CMS 145 updates the composite recipe advice. The CMS145 may retrieve R2R data from MES data store 130 and store theinformation in MICS data store 135.

In another embodiment, CMS 145 may extend its chamber matching effortswith enhanced techniques such as virtual aided R2R control. In oneembodiment with a predictive model, data consolidator 140 consolidatesvirtual data that has been generated by predictor 150. Data consolidator140 may store consolidated data necessary to generate virtual data inMICS data store 135. The methods, models and/or algorithms used togenerate virtual data may depend on the parameter being predicted. Forexample, a first simulation model may be used to predict future valuesof a first parameter, and a second simulation model may be used topredict future values of a second parameter. In one embodiment, CMS 145may use the virtual data and the actual data to generate a compositerecipe advice. In another embodiment, CMS 145 may use the virtual datato control the input and output parameters in a chamber matching effort.

Though the exemplary architecture 100 described above is of amanufacturing environment, embodiments of the present invention may alsooperate in other environments such as an investment environment (e.g.,for trading stocks, bonds, currencies, etc.), a research environment,etc. In such alternative environments, no manufacturing execution systemmay be present, and the manufacturing information and control system mayinstead be a research information and control system, investmentinformation and control system, etc.

FIG. 1B illustrates a multi-dimensional solution to chamber matching,according to some aspects of the present disclosure. FIG. 1B illustratessome dimensions of the chamber matching effort, including, hardwareconfiguration 171, software configuration 172, tool sensors 173, process174, metrology 175, maintenance 176, and end of line electrical 177. Asmentioned above, sustaining a fleet of tools to a matched state canreduce yield losses and yield variability, allow for greater routingflexibility in the fab, identify and control process inefficiencies, andreduce time for root cause analysis of yield issues. A comprehensivesolution to chamber matching includes matching across many dimensions,as depicted in FIG. 1B. Ideally, the matching process provides formatching in every available dimension, from configuration (e.g.,hardware configuration 171 and software configuration 172) throughprocess setup and execution, and yield analysis, as shown in FIG. 1B.The first step in the matching process is to perform a hardware andsoftware audit. In many cases, a “Golden Tool” is identified as part ofa collaborative effort between the customer and operator. The hardwareand software parameters of the golden tool may become a baseline. Adetermination may be made as to what parameters are important to thematching process and what level of matching needs to be obtained.

When the hardware configuration 171 and software configuration 172 arematched, effort may be turned to matching tool sensors 173 and datacollection. Data collection and analysis configurations may be matchedand analysis capabilities such as “chamber variance reporting,” are usedto determine the level of chamber matching as well as investigatesources of any mismatch. Often remedies involve identifyingunderperforming chambers and matching input and output parameters to“golden” chambers. For example in a poly etch matching process, drivecurrent (Ion) matching may be improved from a difference of 7% to 0%.Drive current standard deviation within a wafer may be reduced by 30%.The matching may be achieved by matching gas flows, equipmentconstraints, RF parameters, and recipe optimization.

The capabilities identified above aid the chamber matching process andyield a number of benefits. However, they are generally applied off-lineand may not address chamber matching during production, in for example,process 174, metrology 175, and maintenance 176. As noted above, onedefinition of chamber matching during production is to match outputs bypost process metrology. When only matching the outputs across a fleet,chambers can be operating very differently. An improved definition ofchamber matching, and one used in the disclosure herein, is—chambers arematched if their states of operation are matched. This requires that, inaddition to matching process outputs, process conditions, such asprocess inputs and process variables, are also matched. Further detailsof the improved chamber matching (hereinafter, chamber matching) aredescribed below.

FIG. 2 provides a graphical illustration of chamber matching performedaccording to some aspects of the present disclosure. In the figurebelow, an example with two inputs and one output (e.g., Y=2X₁+10X₂+5) isshown for ease of explanation. It should be noted the use of two inputsand one output is used for the ease of explanation and that chambermatching, as described herein, can be applied to multiple outputs andmultiple inputs. In addition, chamber matching can be performed for morethan 2 chambers. Chamber matching may be performed on a plurality ofchambers of any number.

FIG. 2 illustrates chamber matching using model solution spaces, inaccordance with one embodiment of the present invention. A modelsolution space refers to the range of input values (e.g., parameters) ofa recipe advice that may achieve a specified output value. In thefollowing example, the model solution space output (i.e., thickness) isheld constant. However, in other examples, both the output and the inputvalues of a model solution space may be variable. In graphicalillustration 200, the model solution spaces are represented as straightlines (e.g., model solution spaces 201, 202, and 203). Model solutionspace 201 (e.g., original model solution space) is the model solutionspace for the first process run (e.g., run #1) of chamber 1. The tunableinputs are pressure (e.g., X₂) and power (e.g., X₁), while the output isthickness (e.g., Y). In model solution space 201, the determinedsolution for run #1 is seen at operating point 207 where the inputparameters are approximately as follows: pressure=2.5, power=40.

Model solution space 202 (e.g., current model solution space, Chamber#2) is the model solution space for the first process run (e.g., run #1)of chamber 2. In model solution space 202, the determined solution forrun #1 is seen at operating point 209 where the input parameters areapproximately as follows: pressure=5.5, power=60.

Traditionally, R2R controllers choose a recipe advice that is closest tothe previous advice, without taking into account other chambersperforming the same process. For example, typically a chamber does notproduce an output exactly as the recipe advice proscribes. In FIG. 2,operating point 207 may represent the output (e.g. thickness) of chamber1 after run 1. However, the output may not be exactly what was intended.The intended output may be represented by operating point 205. In atraditional chamber matching approach, a R2R control system may try tomove the operating point 207 (e.g., output) of chamber 1 closer tooperating point 205 by minimally adjusting the tunable inputs (e.g.,pressure and power). The minimal adjustments of a traditional chambermatching approach are represented by arrow 206. An R2R system may chooseany of the input combinations to achieve the desired output asrepresented by model solution space 203. However, under the traditionalchamber matching approach an R2R controller may adjust chamber 1'srecipe advice by making the least adjustments possible while stillapproaching the desired operating point 205.

Graphical illustration 200 illustrates a simplified example of chambermatching in accordance with aspects of the present disclosure, where theadvices of both chamber 1 and chamber 2 are adjusted to achieve a commonoutput value and common input values. As illustrated, both the input andthe output values of both chambers are matched. For example, theoperating point 207 of chamber 1 after run 1 is adjusted, as representedby arrow 204, to operating point 210. Operating point 209 of chamber 2after run 1 is also being adjusted, as represented by arrow 208, to thesame operating point, operating point 210. As illustrated, the operatingpoints of both chamber 1 and 2 are adjusted to operating point 210, andthe inputs of both chambers have been tuned to approach the same values(e.g., approximately pressure=5 and power=40). In one embodiment, therecipe advices of each chamber in the matching group are tied to otherchambers and improve the level of matching across chambers. In anotherembodiment, the chambers are moving towards a composite recipe, X_(c).In another embodiment, parameter weighting and bounding are applied tothe chamber matching effort.

FIG. 3 illustrates a graphical representation of state space as appliedto chamber matching, in accordance with one embodiment of the presentinvention. State space refers to state-space methods of feedback controlin system design and for design optimization. A state space descriptionof system dynamics may provide the dynamics as a set of coupledfirst-order differential equations in a set of internal variables knownas state variables, together with a set of algebraic equations thatcombine the state variables into physical output variables. Saiddifferently, the concept of the state of a dynamic system refers to aminimum set of variables, known as state variables, that fully describethe system and its response to any given set of inputs. By applyingstate space control, additional variables may be controlled in thechamber matching effort. For example, the EWMA model, as discussedpreviously, can be extended to consider such factors as tool-specifichistory, time-dependent tool parameter noise, and time-dependentmetrology measurement noise. State space control as applied to chambermatching may be graphically represented by FIG. 3, where space 301represents the entire solution space (e.g., all possible inputparameters). Space 302 illustrates only the possible solutionssatisfying the constraints. Space 303 illustrates the optimal solutiongiven the minimum value to the objective function. In this case, theobjective function would include chamber matching objectives asdescribed previously (e.g., matching of weighted inputs and outputs).The objectives would be combined with other control objectives in aweighted objective function.

The state space extensions as applied to chamber matching may also bedescribed in mathematical form as follows:y _(t) =A·x _(t) +v _(t)x _(t)+1=x _(t) +w _(t)

where, y_(t)=Metrology measurement at time t, x_(t)=tunable inputs attime t, A=output matrix, w_(t)=tunable input noise, v_(t)=SPCmeasurement noise;

${\min\limits_{x_{t -}x_{t + N}}\;{\sum\limits_{j = 0}^{N}{\left( {y_{Target} - y_{t}} \right)^{T}{Q\left( {y_{Target} - y_{t}} \right)}}}} + {\left( {x_{t + 1} - x_{t}} \right)^{T}{R\left( {x_{t + 1} - x_{t}} \right)}}$

where, y_(Target)=Metrology measurement target, Q=cost matrix fordeviating from metrology measurement target, R=penalty matrix for movingaway from current inputs, Δx_(max)=maximum change in inputs.

  Subject  to $\left. \begin{matrix}{x_{\min} \leq x_{j} \leq x_{\max}} \\{0 \leq {x_{j + 1} - x_{j}} \leq {\Delta\; x_{\max}}}\end{matrix}\rightarrow\begin{matrix}{{{These}\mspace{14mu}{constraints}\mspace{14mu}{are}\mspace{14mu}{designed}}\mspace{14mu}} \\{{{such}\mspace{14mu}{that}\mspace{14mu}{chamber}\mspace{14mu}{matching}}\mspace{14mu}} \\{{criteria}\mspace{14mu}{are}\mspace{14mu}{met}}\end{matrix} \right.$   A ⋅ x_(j) = y_(j) − v_(j)

In one embodiment, matrix A is recipe dependent and tool independent andmay be obtained from experimentation. In one embodiment, the noiseparameters can be entered ahead of time if the distributions are known.In another embodiment, noise parameters can be estimated if not known.As applied to chamber matching, the above equation may be solved inorder to find the optimal input.

FIG. 4 is a flow diagram of one embodiment of a method for chambermatching. The method 400 may be performed by processing logic thatcomprises hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on aprocessing device to perform hardware simulation), or a combinationthereof.

For simplicity of explanation, the methods of this disclosure aredepicted and described as a series of acts. However, acts in accordancewith this disclosure can occur in various orders and/or concurrently,and with other acts not presented and described herein. Furthermore, notall illustrated acts may be required to implement the methods inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be appreciated that themethods disclosed in this specification are capable of being stored onan article of manufacture to facilitate transporting and transferringsuch methods to computing devices. The term “article of manufacture,” asused herein, is intended to encompass a computer program accessible fromany computer-readable device or storage media. In one implementation,method 400 may be performed by chamber matching subsystem 145 as shownin FIG. 1A.

Referring to FIG. 4, method 400 begins at block 402, where processinglogic receives a first chamber recipe advice for a first chamber of aplurality of chambers. The first chamber recipe advice may describe aset of tunable inputs (e.g., temperature and pressure) and a set ofoutputs (e.g., thickness) for a process. Additionally, the first chamberrecipe advice includes a set of first chamber input parameters (e.g.,values for temperature and pressure) to tune the set of tunable inputsand a set of first chamber output parameters (e.g., values of thickness)for the set of outputs.

At block 404 of method 400, processing logic receives a second chamberrecipe advice for a second chamber. The second chamber recipe advicedescribes the set of tunable inputs (e.g., temperature and pressure) andthe set of outputs (e.g., thickness) for the process. The second chamberrecipe advice includes a set of second chamber input parameters (e.g.,different values for temperature and pressure than chamber 1) to tunethe set of tunable inputs and a set of second chamber output parameters(e.g., different values of thickness than chamber 1) for the set ofoutputs.

At block 406 of method 400, processing logic adjusts at least one of theset of first chamber input parameters (e.g., values for temperature andpressure) or the set of second chamber input parameters and at least oneof the set of first chamber output parameters (e.g., values ofthickness) or the set of second chamber output parameters to match theother of the first or second chamber recipe advices. In a simple twochamber example, the output parameters and input parameters of a firstchamber may be adjusted to approximate the output and input parametersof a second chamber. In another embodiment, processing logic may adjustthe input and output parameters of the chambers to approximate the inputand output parameters of a composite chamber recipe, which will bedescribed in more detail below.

In one embodiment, processing logic matches all input parameters andoutput parameters across chambers. In another embodiment, processinglogic matches some but not all the input parameters and some or all ofthe output parameters across chambers.

At block 408 of method 400, processing logic generates a compositechamber recipe advice based on the first and second chamber recipeadvices. A composite chamber recipe advice may be the average of allchamber recipes for a particular process. The composite recipe advicemay include tunable inputs and outputs, and a set of composite inputparameters and a set of output parameters to tune the tunable inputs andoutputs, respectively. Note that the tunable inputs and outputs can bethe same across chambers and the composite chamber recipe advice.However, typically the input and output parameters across chambers andbetween each chamber and the composite chamber recipe advice can vary. Acomposite chamber recipe advice may be used in a chamber matching effortwhere the recipe advice of each chamber is adjusted to approximate thecomposite chamber recipe advice. For example, the input parameters andthe output parameters of a particular chamber recipe advice may be tunedto more closely approximate the input parameters and output parametersof the composite chamber recipe advice.

At block 410 of method 400, processing logic weights at least one inputof the set of tunable inputs and/or at least one output of the set ofoutputs. The tunable inputs and/or outputs of a chamber may be weightedso that the particular weighted inputs or outputs of a chambercontribute more or less to the composite chamber recipe advice. Itshould be noted that for state space, as discussed in regards to FIG. 3,input weights correspond to the matrix R and output weights corresponddo the matrix Q. Additionally, the tunable inputs and/or outputs of achamber may be weighted so that the weighted inputs and/or outputs of aparticular chamber move more or less towards the composite chamberrecipe advice. In one embodiment, weighting of tunable inputs or outputswill allow a chamber to be matched to parameters that are mostimportant. For example, if a customer's most important parameter iscycle time, the tunable input for cycle time may be weighted more thanother tunable inputs.

At block 412 of method 400, processing logic weights chamber recipeadvices of one or more chambers to adjust contribution of individualchambers to the composite recipe advice. In another embodiment,weighting of individual chambers may dictate the level to which aparticular chamber contributes to, or derives advice from, the compositerecipe advice. For example, a particular chamber may be considered a“golden chamber” and as such is chosen to be more heavily weighted sothat it may contribute more than other chambers to the generation of acomposite chamber recipe advice.

At block 414 of method 400, processing logic adjusts a set of chamberinput parameters and a set of chamber output parameters to match thecomposite chamber recipe advice. In one embodiment, processing logic inthe chamber matching effort adjusts each chamber's recipe advice toapproximate the composite chamber recipe advice. For example, processinglogic may adjust the input and output parameters of a chamber so thatthose parameters approach the input and output parameters of thecomposite chamber recipe advice.

At block 416 of method 400, processing logic updates the compositechamber recipe advice based on at least one of the adjusted first orsecond chamber recipe advices. In one embodiment, every time processinglogic makes an R2R recipe advice adjustment to any chamber, thecomposite recipe advice is updated to reflect the adjustment of theparticular chamber. Said differently, each time a chamber's recipeadvice is updated to approximate the composite chamber recipe advice,the composite chamber recipe advice may then be updated to include theupdated chamber recipe advice. The process of updating the compositechamber advice and the chamber recipe advice may be iterative.

In another embodiment, chamber matching may be based on state spacedcontrol as described in FIG. 3. That is, chamber matching may beperformed using state-space control methods where tunable inputs,outputs, and their associated parameters are described by state-spacerepresentations.

FIG. 5 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 500 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The exemplary computer system 500 includes a processing device(processor) 502, a main memory 504 (e.g., read-only memory (ROM), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 506 (e.g., flashmemory, static random access memory (SRAM), etc.), and a data storagedevice 518, which communicate with each other via a bus 508.

Processor 502 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 502 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 502 mayalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processor 502 is configured to execute instructions 526for performing the operations and steps discussed herein.

The computer system 500 may further include a network interface device522. The computer system 500 also may include a video display unit 510(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device 512 (e.g., a keyboard), a cursor controldevice 514 (e.g., a mouse), and a signal generation device 520 (e.g., aspeaker).

The data storage device 518 may include a computer-readable storagemedium 524 on which is stored one or more sets of instructions 526(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 526 may also reside,completely or at least partially, within the main memory 504 and/orwithin the processor 502 during execution thereof by the computer system500, the main memory 504 and the processor 502 also constitutingcomputer-readable storage media. The instructions 526 may further betransmitted or received over a network 574 via the network interfacedevice 522.

In one embodiment, the instructions 526 include instructions forimplementing an chamber matching subsystem 145. While thecomputer-readable storage medium 524 is shown in an exemplary embodimentto be a single medium, the term “computer-readable storage medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present invention.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

In the above description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present invention may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present invention.

Some portions of the detailed description that follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “adjusting”, “generating,” “receiving”, “weighting,”“updating,” or the like, refer to the actions and processes of acomputer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

What is claimed is:
 1. A computer-implemented method, comprising:receiving a first chamber recipe advice for a first chamber of aplurality of chambers, the first chamber recipe advice describing a setof tunable inputs and a set of outputs for a process, wherein the firstchamber recipe advice comprises a set of first chamber input parametersto tune the set of tunable inputs and a set of first chamber outputparameters for the set of outputs; receiving a second chamber recipeadvice for a second chamber of the plurality of chambers, the secondchamber recipe advice describing the set of tunable inputs and the setof outputs for the process, wherein the second chamber recipe advicecomprises a set of second chamber input parameters to tune the set oftunable inputs and a set of second chamber output parameters for the setof outputs; adjusting at least one of the set of first chamber inputparameters or the set of second chamber input parameters and at leastone of the set of first chamber output parameters or the set of secondchamber output parameters to substantially match the first chamberrecipe advice and the second chamber recipe advice; and generating, by aprocessing device, a composite chamber recipe advice based on the firstand the second chamber recipe advices, the composite chamber recipeadvice describing the set of tunable inputs and the set of outputs forthe process, wherein the composite chamber recipe advice comprises a setof composite input parameters to tune the set of tunable inputs and aset of composite output parameters for the set of outputs.
 2. Thecomputer-implemented method of claim 1, further comprising: weighting atleast one of an input of the set of tunable inputs or an output of theset of outputs.
 3. The computer-implemented method of claim 1, furthercomprising: weighting at least one of the first chamber recipe advice orthe second chamber recipe advice.
 4. The computer-implemented method ofclaim 1, wherein at least one of the set of first chamber inputparameters or the set of second chamber input parameters and at leastone of the set of first chamber output parameters or the set of secondchamber output parameters are adjusted based on state space control. 5.The computer-implemented method of claim 1, wherein adjusting at leastone of the first or the second chamber recipe advices comprisesadjusting at least one of the set of first chamber input parameters orthe set of second chamber input parameters and at least one of the setof first chamber output parameters or the set of second chamber outputparameters to substantially match the composite chamber recipe advice.6. The computer-implemented method of claim 5, further comprising:updating the composite chamber recipe advice based on at least one ofthe adjusted first or the adjusted second chamber recipe advices.
 7. Thecomputer-implemented method of claim 1, wherein generating the compositechamber recipe advice comprises generating the set of composite inputparameters by averaging input parameters of the first and the secondchamber recipe advices.
 8. The computer-implemented method of claim 1,wherein generating the composite chamber recipe advice comprisesaveraging the first and the second chamber recipe advices.
 9. Thecomputer-implemented method of claim 1, wherein generating the compositerecipe advice is further based on process conditions of the first andthe second chambers, and wherein the process conditions of the first andthe second chamber are adjusted to substantially match the generatedcomposite recipe advice.
 10. A system comprising: a memory; and aprocessing device coupled to the memory, the processing deviceconfigured to: receive a first chamber recipe advice for a first chamberof a plurality of chambers, the first chamber recipe advice describing aset of tunable inputs and a set of outputs for a process, wherein thefirst chamber recipe advice comprises a set of first chamber inputparameters to tune the set of tunable inputs and a set of first chamberoutput parameters for the set of outputs; receive a second chamberrecipe advice for a second chamber of the plurality of chambers, thesecond chamber recipe advice describing the set of tunable inputs andthe set of outputs for the process, wherein the second chamber recipeadvice comprises a set of second chamber input parameters to tune theset of tunable inputs and a set of second chamber output parameters forthe set of outputs; adjust at least one of the set of first chamberinput parameters or the set of second chamber input parameters and atleast one of the set of first chamber output parameters or the set ofsecond chamber output parameters to substantially match the firstchamber recipe advice and the second chamber recipe advice; and generatea composite chamber recipe advice based on the first and the secondchamber recipe advices, the composite chamber recipe advice describingthe set of tunable inputs and the set of outputs for the process,wherein the composite chamber recipe advice comprises a set of compositeinput parameters to tune the set of tunable inputs and a set ofcomposite output parameters for the set of outputs.
 11. The system ofclaim 10, wherein the processing device is further to: weight at leastone of an input of the set of tunable inputs or an output of the set ofoutputs.
 12. The system of claim 10, wherein the processing device isfurther to: weight at least one of the first chamber recipe advice orthe second chamber recipe advice.
 13. The system of claim 10, wherein atleast one of the set of first chamber input parameters or the set ofsecond chamber input parameters and at least one of the set of firstchamber output parameters or the set of second chamber output parametersare adjusted based on state space control.
 14. The system of claim 10,wherein the processing device is further to: adjust at least one of thefirst or the second chamber recipe advices comprising adjusting at leastone of the set of first chamber input parameters or the set of secondchamber input parameters and at least one of the set of first chamberoutput parameters or the set of second chamber output parameters tosubstantially match the composite chamber recipe advice.
 15. The systemof claim 14, wherein the processing device is further to: update thecomposite chamber recipe advice based on at least one of the adjustedfirst or the adjusted second chamber recipe advices.
 16. The system ofclaim 10, wherein generating the composite chamber recipe advicecomprises generating the set of composite input parameters by averaginginput parameters of the first and the second chamber recipe advices. 17.A non-transitory machine-readable storage medium storing instructionswhich, when executed, cause a processing device to perform operationscomprising: receiving a first chamber recipe advice for a first chamberof a plurality of chambers, the first chamber recipe advice describing aset of tunable inputs and a set of outputs for a process, wherein thefirst chamber recipe advice comprises a set of first chamber inputparameters to tune the set of tunable inputs and a set of first chamberoutput parameters for the set of outputs; receiving a second chamberrecipe advice for a second chamber of the plurality of chambers, thesecond chamber recipe advice describing the set of tunable inputs andthe set of outputs for the process, wherein the second chamber recipeadvice comprises a set of second chamber input parameters to tune theset of tunable inputs and a set of second chamber output parameters forthe set of outputs; adjusting at least one of the set of first chamberinput parameters or the set of second chamber input parameters and atleast one of the set of first chamber output parameters or the set ofsecond chamber output parameters to substantially match the firstchamber recipe advice and the second chamber recipe advice; andgenerating, by the processing device, a composite chamber recipe advicebased on the first and the second chamber recipe advices, the compositechamber recipe advice describing the set of tunable inputs and the setof outputs for the process, wherein the composite chamber recipe advicecomprises a set of composite input parameters to tune the set of tunableinputs and a set of composite output parameters for the set of outputs.18. A non-transitory machine-readable storage medium of claim 17,further comprising: adjusting at least one of the first or the secondchamber recipe advices by adjusting at least one of the set of firstchamber input parameters or the set of second chamber input parametersand at least one of the set of first chamber output parameters or theset of second chamber output parameters to substantially match thecomposite chamber recipe advice.